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Given this code:
.withWatermark("event_time","10 minutes")
.groupBy(window("event_time","15 minutes"))
.count()
What happens to data that arrives after the watermark threshold?
Options:
A. The watermark ensures that late data arriving within 10 minutes of the latest event_time will be processed and included in the windowed aggregation.
B. Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.
C. Data arriving more than 10 minutes after the latest watermark will still be included in the aggregation but will be placed into the next window.
D. Records that arrive later than the watermark threshold (10 minutes) will automatically be included in the aggregation if they fall within the 15-minute window.
Answer: B
Explanation:
According to Spark's watermarking rules:
"Records that are older than the watermark (event time < current watermark) are considered too late and are dropped." So, if a record'sevent_timeis earlier than (max event_time seen so far - 10 minutes), it is discarded.
Reference:Structured Streaming - Handling Late Data
NEW QUESTION # 38
A Spark DataFramedfis cached using theMEMORY_AND_DISKstorage level, but the DataFrame is too large to fit entirely in memory.
What is the likely behavior when Spark runs out of memory to store the DataFrame?
A. Spark will store as much data as possible in memory and spill the rest to disk when memory is full, continuing processing with performance overhead.
B. Spark stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.
C. Spark duplicates the DataFrame in both memory and disk. If it doesn't fit in memory, the DataFrame is stored and retrieved from the disk entirely.
D. Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
When using theMEMORY_AND_DISKstorage level, Spark attempts to cache as much of the DataFrame in memory as possible. If the DataFrame does not fit entirely in memory, Spark will store the remaining partitions on disk. This allows processing to continue, albeit with a performance overhead due to disk I/O.
As per the Spark documentation:
"MEMORY_AND_DISK: It stores partitions that do not fit in memory on disk and keeps the rest in memory.
This can be useful when working with datasets that are larger than the available memory."
- Perficient Blogs: Spark - StorageLevel
This behavior ensures that Spark can handle datasets larger than the available memory by spilling excess data to disk, thus preventing job failures due to memory constraints.
NEW QUESTION # 39
A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior?
Choose 2 answers:
A. Only actions trigger the execution of the transformation pipeline.
B. The Spark engine requires manual intervention to start executing transformations.
C. Transformations are executed immediately to build the lineage graph.
D. Transformations are evaluated lazily.
E. The Spark engine optimizes the execution plan during the transformations, causing delays.
Answer: A,D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Apache Spark employs a lazy evaluation model for transformations. This means that when transformations (e.
g.,map(),filter()) are applied to a DataFrame, Spark does not execute them immediately. Instead, it builds a logical plan (lineage) of transformations to be applied.
Execution is deferred until an action (e.g.,collect(),count(),save()) is called. At that point, Spark's Catalyst optimizer analyzes the logical plan, optimizes it, and then executes the physical plan to produce the result.
This lazy evaluation strategy allows Spark to optimize the execution plan, minimize data shuffling, and improve overall performance by reducing unnecessary computations.
NEW QUESTION # 40
A Data Analyst is working on the DataFramesensor_df, which contains two columns:
Which code fragment returns a DataFrame that splits therecordcolumn into separate columns and has one array item per row?
A)
B)
C)
D)
D. exploded_df = exploded_df.select("record_datetime", "record_exploded")
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To flatten an array of structs into individual rows and access fields within each struct, you must:
Useexplode()to expand the array so each struct becomes its own row.
Access the struct fields via dot notation (e.g.,record_exploded.sensor_id).
Option C does exactly that:
First, explode therecordarray column into a new columnrecord_exploded.
Then, access fields of the struct using the dot syntax inselect.
This is standard practice in PySpark for nested data transformation.
Final Answer: C
NEW QUESTION # 41
A developer initializes a SparkSession:
spark = SparkSession.builder
.appName("Analytics Application")
.getOrCreate()
Which statement describes the spark SparkSession?
A. A new SparkSession is created every time the getOrCreate() method is invoked.
B. If a SparkSession already exists, this code will return the existing session instead of creating a new one.
C. The getOrCreate() method explicitly destroys any existing SparkSession and creates a new one.
D. A SparkSession is unique for each appName, and calling getOrCreate() with the same name will return an existing SparkSession once it has been created.
Answer: B
Explanation:
According to the PySpark API documentation:
"getOrCreate(): Gets an existing SparkSession or, if there is no existing one, creates a new one based on the options set in this builder." This means Spark maintains a global singleton session within a JVM process. Repeated calls to getOrCreate() return the same session, unless explicitly stopped.
Option A is incorrect: the method does not destroy any session.
Option B incorrectly ties uniqueness to appName, which does not influence session reusability.
Option D is incorrect: it contradicts the fundamental behavior of getOrCreate().
(Source: PySpark SparkSession API Docs)
NEW QUESTION # 42
......
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